As used herein, the term “user equipment” is meant broadly and not restrictively, to include any user terminal or, more generally, any device able to connect to a wireless network (a mobile telephone, a personal digital assistant, a smartphone, a tablet computer for example).
By “user speed” is meant here the real speed, of a user or, more generally, of an entity provided with a user equipment attached to a serving base station of the wireless network. For example, the speed of a user provided with a mobile phone, or that of a vehicle including a device connected to a wireless network.
The wireless network may be any cellular or wide-area network (such as WiMAX, GSM, 2G/3G, CDMA, LTE or the like) capable of supporting mobility of user equipments connected thereto.
Estimating the user speed is of crucial interest in such wireless networks. Indeed, the user velocity is a key parameter for different wireless network functions including, among others, mobility and radio resource management. Efficiently estimating the user speed has a high impact on wireless network performances and, consequently, the offered quality of service (QoS).
For instance, regarding mobility management, it is straightforward that handover success rate is directly linked to the user speed: the higher the use speed, the higher is handover frequency with greater risk of call dropping (N. Yaakob et al., “Investigating Mobile Motion Prediction in Supporting Seamless Handover for High Speed Mobile Node”, Proceedings of the International Conference on Computer and Communication Engineering 2008). Accordingly, the optimal adjustment of handover parameters (offsets, hysteresis, timers, and filtering coefficients) should be speed dependent.
The analytical framework proposed by V. Kavitha et al. (“Spatial Queuing for analysis, design and dimensioning of Picocell networks with mobile users”, Performance evaluation, August 2011) illustrates the dependency of the handover losses and of the cell size on the user speed.
Likewise, as regards radio resource management, the most suitable scheduling scheme, either frequency selective or not, depends on the user velocity. Frequency selective scheduling is generally preferred at low user speeds. Otherwise, due to high Doppler conditions, the frequency dependent channel information is not sufficiently accurate. At high speeds, frequency diverse scheduling is preferable.
Thus, as highlighted above by non-exhaustive examples, accurate information on the user velocity is required for optimizing more than one network mechanism.
Up-to-date solutions for user speed estimation within wireless networks are inefficient and do not meet the accuracy requirements due to various reasons.
For instance, those based on capturing speed-dependent short term variations of received signal strengths measurements are inefficient when the period of measurements is higher than the coherence duration of these fast variations. For example, when the period of measurements is higher than the period of Demodulation Reference Signals in LTE (in particular, higher than 1 ms), the maximum velocity that can be detected is upper bounded. For example, at 5 ms period, the maximum velocity that can be detected is 30 kmph. At 10 ms, performances of speed estimation are satisfactory up to 20 kmph, only.
In fact, with regards to the sampling frequency of measurements, prior methods mainly aim at analyzing speed dependent fast fading characteristics: the Doppler frequency is derived from the covariance or the power spectrum of the fast fading channel. But, the Nyquist theorem imposes a high sampling frequency of measurements to avoid spectrum aliasing thus erroneous Doppler estimation. Consequently, these methods are suitable only with short sampling periods.
Moreover, almost known solutions (notably, crossing based methods (Zhang Hong et al., “Mobile speed estimation using diversity combining in fading channels”, Center for Communications and Signal Processing Research, New Jersy Institute of Technology, 2004) and covariance based methods (Rosa Zheng Yahong et al. “Mobile speed estimation for broadband wireless communications over rician fading channels”, IEEE Transactions On Wireless Communications, page 8, January 2009)) are sensitive to noise, especially for small Doppler spreads. As further problems, most of these solutions need the knowledge of the Signal to Noise Ratio (SNR), are limited to Gaussian noise hypothesis, and are complex to implement.
Yet another problem of the prior art is that known solutions need an estimation of the signal power or covariance (as for power spectrum based methods: Baddour Kareem E. et al., “Nonparametric Doppler spread estimation for flat fading channels”, Department of Electrical and Computer Engineering Queen's University, Kingston, ON, CANADA and University of Alberta, Edmonton, CANADA, 2003) which is difficult because it requires the adequate observation windows.
One object of the present invention is to provide a solution to the aforementioned problems, and offers other advantages over the prior art.
Another object of the present invention is to provide a user speed estimation procedure that efficiently copes with large periods of signal strength measurements.
Another object of the present invention is to propose a real time estimation method of the user speed.